**CIFAR**-**10**
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CIFAR-10
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CIFAR-10
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The CIFAR-10 small photo classification problem is a standard
dataset used in computer vision and deep learning.
Although the dataset is effectively solved, it can be used as
the basis for learning and practicing how to develop,
evaluate, and use convolutional deep learning neural networks
for image classification from scratch.
This includes how to develop a robust test harness for
estimating the performance of the model, how to explore
improvements to the model, and how to save the model and later
load it to make predictions on new data.
In this tutorial, you will discover how to develop a
convolutional neural network model from scratch for object
photo classification.
Enter to Rename, Shift+Enter to Preview
The CIFAR-10 small photo classification problem is a standard dataset used in computer vision and deep learning.
Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch.
This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how to save the model and later load it to make predictions on new data.
In this tutorial, you will discover how to develop a convolutional neural network model from scratch for object photo classification.
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#**bold text** CIFAR-10 Photo Classification Dataset
CIFAR is an acronym that stands for the Canadian Institute For
Advanced Research and the CIFAR-10 dataset was developed along
with the CIFAR-100 dataset by researchers at the CIFAR
institute.
The dataset is comprised of 60,000 32×32 pixel color
photographs of objects from 10 classes, such as frogs, birds,
cats, ships, etc. The class labels and their standard
associated integer values are listed below.
0: airplane
1: automobile
2: bird
3: cat
4: deer
5: dog
6: frog
7: horse
8: ship
9: truck
These are very small images, much smaller than a typical
photograph, and the dataset was intended for computer vision
research.
CIFAR-10 is a well-understood dataset and widely used for
benchmarking computer vision algorithms in the field of
machine learning. The problem is “solved.” It is relatively
straightforward to achieve 80% classification accuracy. Top
performance on the problem is achieved by deep learning
convolutional neural networks with a classification accuracy
above 90% on the test dataset.
Enter to Rename, Shift+Enter to Preview
CIFAR is an acronym that stands for the Canadian Institute For Advanced Research and the CIFAR-10 dataset was developed along with the CIFAR-100 dataset by researchers at the CIFAR institute.
The dataset is comprised of 60,000 32×32 pixel color photographs of objects from 10 classes, such as frogs, birds, cats, ships, etc. The class labels and their standard associated integer values are listed below.
0: airplane 1: automobile 2: bird 3: cat 4: deer 5: dog 6: frog 7: horse 8: ship 9: truck These are very small images, much smaller than a typical photograph, and the dataset was intended for computer vision research.
CIFAR-10 is a well-understood dataset and widely used for benchmarking computer vision algorithms in the field of machine learning. The problem is “solved.” It is relatively straightforward to achieve 80% classification accuracy. Top performance on the problem is achieved by deep learning convolutional neural networks with a classification accuracy above 90% on the test dataset.
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Baseline: 1 VGG Blocks
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# example of loading the cifar10 dataset
from matplotlib import pyplot
from keras.datasets import cifar10
# load dataset
(trainX, trainy), (testX, testy) = cifar10.load_data()
# summarize loaded dataset
print('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))
print('Test: X=%s, y=%s' % (testX.shape, testy.shape))
# plot first few images
for i in range(9):
# define subplot
pyplot.subplot(330 + 1 + i)
# plot raw pixel data
pyplot.imshow(trainX[i])
# show the figure
pyplot.show()
# test harness for evaluating models on the cifar10 dataset
import sys
from matplotlib import pyplot
from keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
to_categorical([0,1,2,3,4,5,6,7,8,9], num_classes=10)
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dense
from keras.layers import Flatten
from tensorflow.keras.optimizers import SGD
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# load train and test dataset
def load_dataset():
# load dataset
(trainX, trainY), (testX, testY) = cifar10.load_data()
# one hot encode target values
trainY = to_categorical(trainY)
testY = to_categorical(testY)
return trainX, trainY, testX, testY
# scale pixels
def prep_pixels(train, test):
# convert from integers to floats
train_norm = train.astype('float32')
test_norm = test.astype('float32')
# normalize to range 0-1
train_norm = train_norm / 255.0
test_norm = test_norm / 255.0
# return normalized images
return train_norm, test_norm
# define cnn model
def define_model():
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))
model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(10, activation='softmax'))
# compile model
opt = SGD(lr=0.001, momentum=0.9)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
return model
# plot diagnostic learning curves
def summarize_diagnostics(history):
# plot loss
pyplot.subplot(211)
pyplot.title('Cross Entropy Loss')
pyplot.plot(history.history['loss'], color='blue', label='train')
pyplot.plot(history.history['val_loss'], color='orange', label='test')
# plot accuracy
pyplot.subplot(212)
pyplot.title('Classification Accuracy')
pyplot.plot(history.history['accuracy'], color='blue', label='train')
pyplot.plot(history.history['val_accuracy'], color='orange', label='test')
# save plot to file
filename = sys.argv[0].split('/')[-1]
pyplot.savefig(filename + '_plot.png')
pyplot.close()
# run the test harness for evaluating a model
def run_test_harness():
# load dataset
trainX, trainY, testX, testY = load_dataset()
# prepare pixel data
trainX, testX = prep_pixels(trainX, testX)
# define model
model = define_model()
# fit model
history = model.fit(trainX, trainY, epochs=10, batch_size=64, validation_data=(testX, testY), verbose=1)
# evaluate model
_, acc = model.evaluate(testX, testY, verbose=1)
print('> %.3f' % (acc * 100.0))
# learning curves
summarize_diagnostics(history)
# entry point, run the test harness
run_test_harness()
/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. super(SGD, self).__init__(name, **kwargs)
Epoch 1/10 782/782 [==============================] - 158s 200ms/step - loss: 1.7703 - accuracy: 0.3701 - val_loss: 1.5234 - val_accuracy: 0.4502 Epoch 2/10 782/782 [==============================] - 147s 188ms/step - loss: 1.4316 - accuracy: 0.4887 - val_loss: 1.3362 - val_accuracy: 0.5247 Epoch 3/10 782/782 [==============================] - 146s 187ms/step - loss: 1.2692 - accuracy: 0.5512 - val_loss: 1.2333 - val_accuracy: 0.5636 Epoch 4/10 782/782 [==============================] - 146s 187ms/step - loss: 1.1612 - accuracy: 0.5898 - val_loss: 1.1687 - val_accuracy: 0.5855 Epoch 5/10 782/782 [==============================] - 146s 187ms/step - loss: 1.0767 - accuracy: 0.6222 - val_loss: 1.1117 - val_accuracy: 0.6156 Epoch 6/10 782/782 [==============================] - 146s 187ms/step - loss: 1.0101 - accuracy: 0.6469 - val_loss: 1.0784 - val_accuracy: 0.6203 Epoch 7/10 782/782 [==============================] - 146s 187ms/step - loss: 0.9523 - accuracy: 0.6656 - val_loss: 1.0318 - val_accuracy: 0.6395 Epoch 8/10 782/782 [==============================] - 146s 187ms/step - loss: 0.8932 - accuracy: 0.6881 - val_loss: 1.0036 - val_accuracy: 0.6536 Epoch 9/10 782/782 [==============================] - 146s 187ms/step - loss: 0.8418 - accuracy: 0.7083 - val_loss: 0.9943 - val_accuracy: 0.6575 Epoch 10/10 782/782 [==============================] - 150s 192ms/step - loss: 0.7963 - accuracy: 0.7257 - val_loss: 0.9688 - val_accuracy: 0.6632 313/313 [==============================] - 8s 25ms/step - loss: 0.9688 - accuracy: 0.6632 > 66.320
# plot diagnostic learning curves
def summarize_diagnostics(history):
# plot loss
pyplot.subplot(211)
pyplot.title('Cross Entropy Loss')
pyplot.plot(history.history['loss'], color='blue', label='train')
pyplot.plot(history.history['val_loss'], color='orange', label='test')
# plot accuracy
pyplot.subplot(212)
pyplot.title('Classification Accuracy')
pyplot.plot(history.history['accuracy'], color='blue', label='train')
pyplot.plot(history.history['val_accuracy'], color='orange', label='test')
# save plot to file
filename = sys.argv[0].split('/')[-1]
pyplot.savefig(filename + '_plot.png')
pyplot.show()
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